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2019 | OriginalPaper | Buchkapitel

Recognizing Human Actions Using 3D Skeletal Information and CNNs

verfasst von : Antonios Papadakis, Eirini Mathe, Ioannis Vernikos, Apostolos Maniatis, Evaggelos Spyrou, Phivos Mylonas

Erschienen in: Engineering Applications of Neural Networks

Verlag: Springer International Publishing

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Abstract

In this paper we present an approach for the recognition of human actions targeting at activities of daily living (ADLs). Skeletal information is used to create images capturing the motion of joints in the 3D space. These images are then transformed to the spectral domain using 4 well-known image transforms. A deep Convolutional Neural Network is trained on those images. Our approach is thoroughly evaluated using a well-known, publicly available challenging dataset and for a set of actions that resembles to common ADLs, covering both cross-view and cross-subject cases.

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Vorheriges Kapitel On Predicting Bottlenecks in Wavefront Parallel Video Coding Using Deep Neural Networks

Nächstes Kapitel Staircase Detection Using a Lightweight Look-Behind Fully Convolutional Neural Network

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Titel: Recognizing Human Actions Using 3D Skeletal Information and CNNs
verfasst von: Antonios Papadakis
Eirini Mathe
Ioannis Vernikos
Apostolos Maniatis
Evaggelos Spyrou
Phivos Mylonas
Verlag: Springer International Publishing
Buch: Engineering Applications of Neural Networks
Print ISBN: 978-3-030-20256-9

Electronic ISBN: 978-3-030-20257-6

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-20257-6_44

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